Demodulation

ABSTRACT

Demodulation apparatus includes a transversal filter characterized by coefficients adjusted by a coefficient determiner responsive to a constant modulus error signal and variable mode error signal related to the output of a spectral mean frequency detector having its input coupled to the output of the transversal filter.

The present invention relates in general to recovering data frommodulated signals. More particularly, the present invention concernsreducing the effects of multipath and adjacent channel interference, andother linear time invariant (or slowly time varying) operations that maybe applied intentionally or unintentionally to a modulated signal. Theinvention finds particular use as an adaptive transmission channelequalizer, where it operates to reduce anomalies in a received signalthat result from nonidealities in a transmission channel. The inventioncomprises an adaptive filter structure configured to act as an adaptiveequalizer that uses a novel method of adaptation, wherein the error costfunction used to adjust the coefficients of the adaptive filter isitself adjusted, based on characteristics of the received signal.

The invention has utility in any system where it is desired to recoverdata from a modulated signal, where the modulated signal has thefollowing characteristics; 1) A frequency spectrum magnitude that isapproximately flat over the modulated signal bandwidth, and 2) A signalmagnitude that is substantially constant over time (constant or nearconstant modulus). The invention has particular utility when used in aradio receiver designed to receive and demodulate frequency modulated(FM) signals, although use in systems with alternate modulation schemesis also advantageous (use as an adaptive channel equalizer in digitalcommunication systems is one additional example).

BACKGROUND OF THE INVENTION

Multipath interference in the time domain may be represented by thesuperposition of an initial version of a signal of interest (SOI) withmultiple delayed and filtered versions of the same signal. The delayedand filtered versions of the SOI result from reflections and otherirregularities. When viewed in the frequency domain, multipathinterference can be modeled as a comb filter placed somewhere in thetransmission path of an SOI.

FIG. 1 is a block diagram of a prior art signal processing system thatattempts to reduce the effects of multipath interference on a receivedsignal. This system places an adaptive transversal (or FIR) filter inthe receiver signal path whose function is to undo linear errors causedby multipath interference.

Equations 1 through 4 below provide the mathematical foundation for aprior art adaptive transmission channel equalizer based on a ConstantModulus Algorithm (CMA). CMA refers to the type of error estimate usedto adjust the transversal filter coefficients. These equationsspecifically refer to a form of CMA referred to as CMA 2-2 (afterDominique Goddard), but the discussion can be generalized to other formsof CMA known in the prior art.y(k)=W ^(T)(k)X(k)   Eq. 1W(k+1)=W(k)−μe _(cm)(k)X*(k)   Eq. 2e _(cm)(k)=[|y(k)|² −R ₂ ]y(k)   Eq. 3R ₂ =E{|y(k)⁴ }/E{″y(k)|²}  Eq. 4

In equations 1 through 4 above, X(k) is a vector of input history attime k, y(k) is the scalar output value from the adaptive filter, W(k)is a vector of filter coefficients, e_(cm)[k] is the Constant ModulusError Estimate, which is based on the CMA 2-2 algorithm, R₂ is aconstant that is dependent upon the data modulation method used, andrepresents the scaling necessary to match the adaptive filter output tothe thresholds of the data demodulator. For Constant Modulus modulationmethods such as frequency modulation (FM), this expression reduces toR₂=R_(o) ², where R_(o)=E{|y(k)|}. μ is a step size parameter thatadjusts the extent to which coefficients are changed at each time step.Each next set of coefficients W(k+1) is determined based on the previouscoefficient values W(k) and an estimate of the gradient of the error ofthe filter output modulus with respect to the transversal filter tapweights (coefficients).

In prior art system 100 of FIG. 1, an input signal is received by frontend block 10. The front end serves as an interface between the outsideworld and the system of interest. A typical front end may incorporate asignal conditioning function and some type of tuning function. Signalconditioning is usually applied to match the incoming signal from theoutside world to the system of interest, in order to enhance systemdynamic range and signal to noise ratio (an example is use of an AGCfunction in the RF front end of a radio receiver). Tuning usuallyconsists of combining a frequency selective function with a frequencyshifting function, where the shifting function is used to shift therange of frequencies over which the frequency selectivity occurs.

The output of frontend 10 feeds into power normalization block 20. Insome systems, power normalization may be performed directly by frontend10 (in the AGC if present). In such systems, a separate powernormalization stage is not required. However, it is often the case theoutput of frontend 10 may still have an average signal power that variesconsiderably with time. In these cases, a separate power normalizationfunction is applied.

FIG. 1 shows power normalization 20 located ahead of the ADC 30. Powernormalization 20 may be accomplished by an analog AGC. In system 100 ofFIG. 1, the power normalized signal (the output of power normalizationblock 20) is then digitized by ADC 30 and applied to the input ofadaptive transversal filter 40. Eq. 1 defines the signal processing oftransversal filter 40 given the input data and coefficient vectors. Theoutput of adaptive transversal filter 40 splits into two paths, a signalpath and a control path. The signal path output is demodulated bydemodulation block 70 in a manner that represents the inverse of theused to original modulation. After demodulation, the demodulated signalmay also be decoded, if the original modulating signal was encoded insome manner. The output of demodulator 70 is the recovered data signalof interest.

The control path output feeds back into Constant Modulus (CM) ErrorEstimation block 60. CM error estimation block 60 determines an errorcost function in accordance with Eq. 3, which effectively is thedifference between the modulus of the output of adaptive filter 40 and aconstant value R₂ (as defined in Eq. 4). The result of thisdetermination is the error estimate signal e_(cm)(k). The error estimatesignal e_(cm)(k) (the output of CM error estimation block 60) feeds intocoefficient determination block 50. Coefficient determination block 50determines coefficient signal updates in accordance with Eq. 2. Thecoefficient signal values are updated based on the error estimate signale_(cm)(k), the current coefficient signal values W(k), the adaptationstep size μ, and the conjugated input data signal X*(k). Whenfunctioning as intended, the coefficient value signals determined atsucceeding time steps change in a direction that tends to minimize theerror cost function.

Prior art system 100 of FIG. 1 assumes that the transmitted SOI has aconstant modulus property. When such a signal is subject to multipathinterference (during transmission, for example), the received signalwill no longer have a constant modulus property. (The comb filterbehavior caused by multiple reflections destroys the constant modulusbehavior). CM error estimation block 60 of prior art system 100 inconjunction with coefficient determination block 50 determinescoefficient signals for transversal filter 40 that attempt to force theoutput of transversal filter 40 to have a constant modulus property.Algorithms that accomplish this behavior are referred to as CMA(constant modulus algorithms).

For additional background reference is made is made to the following:Number Title Inventor Date U.S. Pat. No. CMA-Based Antenna Yukitomo, May25, 1999 5907303 System et al. U.S. Pat. No. Technique for Werner, Sep.15, 1998 5809074 Improving the Blind et al. Convergennce of an AdaptiveEqualizer Using a Transition Algorithm U.S. Pat. No. Generalized Werner,Aug. 17, 1999 5940440 Multimodulus et al. Technique for BlindEqualization U.S. Pat. No. Reducing Multipath Tingley Dec. 9, 19975697084 Fading Using Adaptive Filtering U.S. Pat. No. Method and Devicefor Godard Jan. 5, 1982 4309770 Training an Adaptive Equalizer by Meansof an Unknown Data Signal in a Transmission System Using DoubleSideband-Quadrature Carrier Modulation U.S. Pat. No. Adaptive EqualizingHwang Apr. 9, 1996 5506871 System for Digital et al. Communications EP 0854 Adaptive Antenna Akaiwa, Jul. 22, 1998 589 A2 Diversity Receiver etal

OTHER PUBLICATIONS

J. R. Treichler and B. G. Agee, “A New Approach to Multipath Correctionof Constant Modulus Signals,” IEEE Trans. On ASSP, Vol 31, No.2, pps.459-472, April 1983.

Richard P. Gooch and Brian Daellenbach, “Prevention of InterferenceCapture in a Blind (CMA-Based) Adaptive Receive Filter,” ConferenceRecord of the 23rd Asilomar Conference on Signals, Systems andComputers, Maple Press, pps. 898-902, November 1989.

Y. S. Choi, H. Hwang, and D. I. Song, “Adaptive Blind EqualizationCoupled with Carrier Recovery for HDTV Modem,” IEEE Transactions onConsumer Electronics, Vol. 39, No. 3, pps. 386-391, August 1993.

Widrow, B. and Stearns, S. (1985). Adaptive Signal Processing.Prentice-Hall.

It is an important object of the invention to provide improved methodsand means for adaptive transversal filtering.

SUMMARY OF THE INVENTION

The present invention analyzes the received signal to determine if anoverlapping, adjacent interfering signal is present. If an interferingsignal is detected, the initialization of the adaptive filter is alteredin a manner that significantly improves the ability of the filter toconverge to the solution that selects the desired signal and rejects theinterfering signal. The process that alters the initialization is calledaltering the mode of the adaptive filter. The present inventioncomprises a mode controlled blind adaptive transversal filter. Theinvention substantially improves the ability of an adaptive transversalfilter to converge to a desired solution when operating under nonidealconditions (i.e. conditions that give rise to a solution space with morethan one minimum).

Other features, objects and advantages will become apparent from thefollowing detailed description when read in connection with theaccomanying drawing in which:

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING

FIG. 1 is a block diagram of a prior art system that uses a constantmodulus algorithm to adjust coefficients of a transversal filter placedin the path of the signal of interest;

FIG. 2 a is a block diagram of the mode controlled blind adaptive filterof the present invention, with power normalization occurring beforeanalog-to-digital conversion;

FIG. 2 b is a block diagram of an alternative form of the modecontrolled blind adaptive filter of the current invention, with powernormalization occurring after analog-to-digital conversion;

FIG. 3 a is a block diagram of a simplified spectral mean frequencydetector;

FIG. 3 b is a block diagram of an idealized prior art FM detector;

FIG. 4 a is a block diagram representation of the operation of avariable mode controller for determining the mode control variablesignal a(k);

FIG. 4 b is a graph of the mode control variable α(k) vs. the spectralmean frequency (SMF) of the output of the adaptive transversal filter;

FIG. 5 is a block diagram of a prior art FM stereo receiver using aconstant modulus algorithm to adjust coefficients of a transversalfilter placed in the path of the SOI;

FIG. 6 a is a block diagram of an exemplary embodiment of the modecontrolled blind adaptive filter of the present invention in an FMstereo receiver, where the power normalizing AGC is implemented inanalog form and is located before analog-to-digital conversion; and

FIG. 6 b is a block diagram of an alternative embodiment of the modecontrolled blind adaptive filter of the present invention in an FMstereo receiver, where the power normalizing AGC is implemented indigital form and is located after analog-to-digital conversion.

DETAILED DESCRIPTION OF THE INVENTION

Eq. 1 in combination with Eqs. 5-8 provide the mathematical foundationfor the Mode Controlled Blind Adaptive Transversal Filter of the presentinvention. FIG. 2 a is a block diagram of an embodiment of theinvention. FIG. 2 b is a block diagram of an alternative embodiment ofthe invention.

The coefficients characterizing the transversal filter are stored in avector W. For a filter characterized by N coefficients, the vector Wwill have N elements. The updates of L arbitrary coefficient vectorelements, where N>L≧1, is determined in accordance with Eqs. 2-4 (i.e.one or more coefficients are updated in accordance with the constantmodulus error estimate e_(cm)). The remaining N-L coefficient elementsare updated in accordance with equations 6 and 7 below. The subscript grefers to the L coefficients updated in accordance with Eqs. 2-4, and hto refer to the N-L other coefficients updated using the variable modeerror estimate e_(vm) in accordance with Eq. 7. The L coefficients donot need to be continuously indexed elements in the coefficient vectorW. The L elements chosen can be any arbitrary subset of the Ncoefficient elements.

w _(g)(k+1)=w _(g)(k)−αe _(cm)(k)x*(k), where   Eq. 5

w, α, e_(cm)(k), and x(k) were defined earlier.w _(h)(k+1)=w _(h)(k)−αe _(vm)(k)x*(k−i)   Eq. 6e _(vm)(k)=[|y(k)²−(1−α(k))R]y(k) where;   Eq. 7

The e_(vm)(k) signal is referred to as the variable mode error estimatesignal, the α(k) signal is referred to as the mode variable signal, andR is an arbitrary constant value. The invention is not limited in thechoice of R. In an exemplary embodiment, R is set to the value of R₂,which is defined in Eq. 4. In the exemplary embodiment where theinvention is used in an FM receiver, the E[y(k)]32 R_(o), so that thevalue of R₂ becomes R_(o). Note that R_(o) is a constant for FM.

Furthermore, in an exemplary embodiment, the first coefficient w_(o) isadjusted in accordance with a constant modulus algorithm in accordancewith Eqs. 2-4, and all other coefficient signals are adjusted using thevariable mode error criteria in accordance with Eqs. 6-7.

In the system of FIG. 2 a, an input signal is first received by frontend 210, which operates similarly to frontend 10 of prior art system 100of FIG. 1. The output of front end 210 then passes through powernormalization block 220. Since this function is located ahead of ADC230, it is typically implemented in analog form. An AGC circuit can beused to accomplish a power normalization function. AGC circuits areknown in the prior art, and will not be described in great detail.

FIG. 2 b places power normalization 320 after ADC 330. Again, an AGCcircuit could be used to provide the power normalization function. Inthis case, the AGC function typically in digital form. Techniques foraccomplishing a digital AGC function are well known, and will not bedescribed in further detail here.

Power normalization functions of blocks 220 and 320 described abovepreferably occur as a function of time at a rate sufficiently slowerthan the rate at which the adaptive filter coefficients are updated sothat the adaptive filter converges to a solution.

Other embodiments may accomplish power normalization with the algorithmthat adjusts filter coefficients. An example of such an algorithm is thenormalized LMS algorithm (NLMS). Power normalization is accomplished inNLMS by dividing the adaptation step size parameter m (which is aconstant in LMS) by an estimate of the power contained in the computederror signal. One skilled in the art could readily adapt this powernormalization technique for use in CMA.

The invention is not limited in the form of the power normalizationfunction used. The invention is also not limited in the location of thepower normalization function. Power normalization can be accomplished inAGC's placed before or after analog to digital conversion, or includedas part of the adaptive filter algorithm. An exemplary embodiment usesan analog AGC located ahead of the ADC. Placing the AGC at this locationhas the advantage of enhancing the dynamic range of the ADC.

Power normalization located in the signal path operates slightlydifferently than power normalization performed within the adaptivealgorithm because power normalization within the adaptive algorithmnormalizes the power in the error signal, not the SOI. The effect of thepower normalization in each case, however, is essentially the same. Allof the power normalization methods describe are used to improve theadaptation performance of the system, and all of the described methodsaccomplish similar improvements.

In FIG. 2 a, the output of power normalization block 220 is thendigitized by ADC 230, and the output of ADC 230 is fed to the input oftransversal filter 240, and to coefficient signal determination block250. In FIG. 2 b, the output of front end 310 is digitized by ADC 330.The output of ADC 330 is fed to power normalization block 320, whichthen feeds the input of transversal filter 340 and coefficientdetermination block 350.

Transversal filters 240 and 340 process signals in accordance with Eq. 1to provide outputs dependent on the input data and the coefficientvector. The outputs of transversal filters 240 and 340 feed into datademodulators 270 and 370, and into spectral mean frequency detectors(SMFD) 272 and 372, respectively. The function of data demodulators 270and 370 is to recover the desired modulation. The invention can be usedwith numerous modulation methods, and is not limited to any specificmethod. In the exemplary embodiment describing use of the invention in astereo RF FM receiver, the data demodulator comprises an FM detector.

Additional decoding of the demodulated signal may be required, if themodulating signal was encoded. The invention is not limited in the formof encoding and decoding with which it may be used.

SMFD 272 and 372 detect the mean spectral frequency of the output oftransversal filters 240 and 340. This signal is used as an indication ofthe presence of adjacent channel interference. Any detector thatpossesses the ability to detect the mean spectral frequency of a signalcan be used here, and the invention is not limited to using any oneparticular type of detector. Detectors can be realized for use in thecurrent invention in analog or digital form, and may be implemented inhardware or softwarem, or combination thereof.

An SMFD can be implemented as shown in FIG. 3 a. SMFD 110 of FIG. 3 a iscomprised of instantaneous frequency detector 51, which is implementedas an FM detector in an exemplary embodiment (FM detectors typicallydetect the instantaneous frequency of a signal), followed by anaveraging operation, which is accomplished by low pass filter 52. Lowpass filtering the output of an FM detector averages the output of theFM detector, which results in a signal representative of the meanspectral frequency. Low pass filter 52 may be a simple first order lowpass filter. However, the invention is not limited in the type of lowpass filter used. The low pass filter used in SMFD 110 is not requiredto meet any specific passband and stopband characteristics. Furthermore,the low pass filter can perform scaling if desired. Other processingcould be substituted for low pass filter 52 to perform an averagingfunction (such as a moving average filter). The invention is not limitedin the manner of processing used to accomplish an averaging function.

In the exemplary embodiment of an FM receiver, the FM detector used todetect the instantaneous frequency is also used as the data demodulator.This is shown in FIGS. 6 a and 6 b. The outputs of FM detectors 870 and970 are each split into two paths. One path leads to low pass filter 875and 975 (which are portions of the SMFD), as described above. The otherpath is the desired signal, which may be decoded as needed.

FM detector 120 is shown in FIG. 3 b. FM detectors can be implemented inanalog or digital form, in hardware or in software, or combinationsthereof. The invention is not limited in the manner in which an FMdetector is implemented. Arg block 61 of FM detector 120 first extractsthe argument (or angle) of the modulated signal. Unwrap block 62 thenunwraps the extracted angle to remove discontinuities. d/dt block 63then differentiates the unwrapped angle with respect to time. The outputof the differentiation is then scaled by gain block 64. The output ofgain block 64 is the signal of interest (the recovered data).

FM detectors are widely known in the prior art, and specific embodimentswill not be described in further detail here. An FM detector could beimplemented in the current invention using any one of a number of knowntechniques.

SMFD outputs 272 and 372 are input to variable mode controller blocks274 and 374 respectively, which use this information to determine how tovary the operating mode of the adaptive filter. The output of variablemode controller blocks 274 and 374 is the mode variable signal α(k). Theα(k) signal is a function of the SMFD output. The α(k) signal ispreferably a positive, semidefinite, monotonic, and symmetric about zerofunction of the output of the SMFD. There are a large number offunctions that could be created that have the above properties, any ofwhich could be applied to the invention. Any function or functions thatsatisfy the stated preferences would work in the invention, and theinvention is not limited in the form of the function used to meet thesepreferences.

In an exemplary embodiment, the range of values for α(k) is preferablybounded between zero and 1 (although the invention is not restricted tooperation only over this range of values). Furthermore, the exemplaryembodiment implements a small dead band, where α(k) is held to a zerovalue for small values of the SMFD output. The operations of boundingthe range of values of α(k) and adding a dead band region are notrequired in order for a mode controlled blind adaptive filter tofunction. Inclusion of these features does however, improve the abilityof the system to adapt to the desired signal.

FIG. 4 a is a block diagram representing the determination of the α(k)signal in an exemplary embodiment. It is convenient to assume that theSMFD used to detect the mean spectral frequency is operating at basebandfrequencies. For an FM signal at baseband, complex frequency modulationresults in the instantaneous frequency of the modulated signal havingboth positive and negative frequency deviations about center frequency.The output of the SMFD will be a varying DC level about zero.

The current invention could operate at IF or RF frequencies if desired.This would only add an offset to the output of the SMFD, which couldeasily be removed in a subsequent stage. However, it is convenient tooperate at baseband, as removal of this offset is not required. Thecomplete system comprising the invention could be scaled to work at IFor RF frequencies, without loss of generality.

The signal at the output of low pass filter 52 of the SMFD will besymmetric about zero (for operation at baseband) and monotonic. This isthe input to variable mode controllers 274 and 374, whose operation isfurther described in connection with FIG. 4 a. Variable mode controller130 of FIG. 4 a first takes the absolute value of the low pass filteroutput, in absolute value block 71. Taking the absolute value gives theresulting signal a positive semidefinite characteristic. The signal atthis point possesses the characteristics for the invention to functionas intended.

An exemplary embodiment adds an offset constant 73 to the output ofabsolute value block 71 using summer 72. The offset is used to provide adead band about 0 for α(k). The amount of offset used may be varied tofine tune operation of the invention for particular applications. Thedeadband will keep the α(k) signal value equal to zero for smallvariations about zero of the SMFD output.

The resulting signal then passes through gain block 74 and saturationblock 75. Gain is another parameter that can be adjusted to fine tunebehavior of the system for particular applications. Saturation block 75in an exemplary embodiment bounds the resulting signal to values betweenzero and 1, although other range values could be chosen if desired. Agraph showing the relationship between the SMFD output and α(k) in thepreferred embodiment is shown in FIG. 4 b.

The outputs of transversal filters 240 and 340 and variable modecontrollers 274 and 374 are the inputs to Variable Mode Error Estimationblocks 273 and 373. The output of Variable Mode Error Estimation blocks273 and 373 is the variable mode error estimate signal e_(vm).Determination of e_(vm) is based on these inputs and the value of theconstant R (which is expected value of the modulus of the transversalfilter output, and is equal to R_(o) in an exemplary embodiment), inaccordance with Eq. 7.

The inputs to Constant Modulus Error Estimation blocks 260 and 360 arethe outputs of transversal filters 240 and 340 respectively. Theconstant modulus error estimate is determined in accordance with Eq. 3,and is the same as was described with regard to the Constant Moduluserror block 60 of FIG. 1.

The outputs of the error estimate blocks 260, 360, 273, 373, and thedigitized input data, all feed into coefficient determination blocks 250and 350. Coefficient determination blocks 250 and 350 determinecoefficient update signals in accordance with Eqs. 5 and 6. Eq. 5describes the update determination for the L transversal filtercoefficient(s) that is (are) to be updated in accordance with a constantmodulus error criteria, and Eq. 6 describes the update calculation forthe N-L transversal filter coefficients that are to be updated inaccordance with a variable mode error criteria. In an exemplaryembodiment, the first filter coefficient is updated in accordance withEq. 5, and all other coefficients are updated in accordance with Eq. 6.

The mode variable ax(k) signal is used to modify the magnitude of theconstant R used in the determination of the e_(vm)[k] signal. That is,the α(k) signal modifies the error cost function signal that is used toadjust the N-L adaptive transversal filter coefficient signals that aredesignated to be updated based on the variable mode error criterion(i.e. the α(k) signal varies the mode of the adaptive filter). The α(k)signal is a variable that can be controlled in some manner. When theα(k) signal is set to 0, e_(vm)[k]=e_(cm)[k], and the variable modeerror based coefficient signal is identical to the constant moduluserror based coefficient update signal. In the exemplary embodiment, forthe case where the α(k) signal=0, the variable mode error criteria isidentical to the constant modulus error criteria, and operation of thesystem degenerates into CMA 2-2 behavior.

The operating mode of the adaptive transversal filter may be heldconstant or allowed to vary, by either; a) holding the α(k) signalconstant or b) allowing the α(k) signal to vary with time in somemanner. The invention is not limited in the manner in which the value ofthe mode variable is changed over time. The exemplary embodiment of theinvention allows the mode variable to vary with time.

As stated earlier for the exemplary embodiment (where the first filtercoefficient signal is updated using a constant modulus error criteriaand the remaining coefficient signals are updated using a variable modeerror criteria), when the mode variable is set to zero, the systembehaves as a CMA 2-2 based adaptive filter. When the mode variable isset to 1, the adaptive filter behaves in a manner referred to as a“Kurtosis Whitener”. Signal whiteners are known in the prior art (asingle step linear predictor is one example), and they are often used toestimate the inverse of the energy present in a signal as a function offrequency. This use is also the function of a Kurtosis Whitener in theinvention.

The Kurtosis Whitener minimizes the correlation of the cube (thirdpower) of the filter output against all taps of the adaptive filterexcept the first, which in effect minimizes the normalized Kurtosis ofthe filter output (with the constraint that the average modulus is heldat or near R). The Kurtosis Whitener generates a smooth estimate of theinverse of the signal energy.

The exemplary embodiment of the invention can vary its behavior betweenoperating as a CMA-based adaptive filter and operating as a KurtosisWhitener. Stated another way, the system can change its behavior fromacting to invert the transfer function of linear processing locatedanywhere in the signal path between the output of a modulation operationand the input to the adaptive filter, to inverting the spectrum of thesignal present at the filter input.

The behavior of the exemplary embodiment of the invention for modevalues between zero and one is difficult to exactly describe. The systemhas characteristics that vary between those of either extreme discussedabove. That is, the behavior of the complete system smoothly variesbetween inverting the transfer function of linear processes locatedbetween the modulator and the transversal filter, and inverting thespectrum of the signal present at the transversal filter input, as theα(k) signal varies.

The behavior of the invention where a first arbitrary subset ofcoefficient signals are updated based on a constant error criteria and asecond complimentary set of coefficient signals are updated based on avariable mode error criteria is also difficult to exactly describe. Thebehavior of the system for the case where L is small compared to N willbe similar to the behavior where L=1, described above for the exemplaryembodiment.

The invention operates over a range of mode values, rather thanswitching between a few different discrete values (say zero and one).The mode value controls the determination of the error estimate for theadaptive filter. Changes in the mode value preferably occur smoothly.

Preferably, when a strong interfering signal is present, the adaptivefilter initially acts as a signal whitener. This is accomplished in theinvention by the action of SMFD blocks 272 and 372, and variable modecontrol blocks 274 and 374. When an interfering signal is detected, theoutput of SMFD 272 and 372 will be nonzero, and the mode variable α(k)signal will increase from zero to some positive value (between zero andone in the exemplary embodiment), depending on the strength of theinterfering signal relative to the desired signal. The mode of theadaptive filter of the exemplary embodiment varies so that it behavesmore like a Kurtosis Whitener when an adjacent interfering signal isdetected. The stronger the interfering signal, the larger the value ofthe α(k) signal and the closer to Kurtosis Whitening the adaptive filterbehavior gets.

Whitening forces the adaptive filter to attenuate the energy due to theinterfering signal with respect to the desired signal, when theinterfering signal is strong. That is, the coefficients of thetransversal filters 240 and 340 have been adjusted by variable modecontrollers 274 and 374 to find a solution that attenuates theinterfering signal with respect to the desired signal, when theinterfering signal is strong. This solution will have a location on theerror surface that is closer to the location of the local minimumsolution that favors the desired signal and rejects the interferingsignal.

As the signal is whitened, the output of adaptive filters 240 and 340,on which SMFD 272 and 372 perform detection, will have a flat spectrum.The flattening (or whitening) operation reduces the level of theinterfering signal present at the filter outputs compared to the desiredsignal, and as a result the outputs of SMFD 272 and 372 accordingly. Asthe outputs of SMFD 272 and 372 decreases the (α(k) signal trends backtoward zero), the error cost function signal will approach CMAoperation. An advantage of the invention is that in this case, the errorcost function signal is approaching CMA, but the coefficient signalswere effectively initialized to a point from which CMA will find thedesired solution. As the system adjusts coefficient signals further, andthe solution approaches the desired solution, the interfering signalpresent at the outputs of transversal filters 240 and 340 is furtherattenuated. The output of SMFD 272 and 372 drops further to the pointwhere the deadband function included in the calculation of the α(k)signal sets the α(k) signal to zero.

FIG. 5 a is a block diagram of a prior art FM RF receiver that attemptsto reduce the effects of multipath interference on a received FM stereoradio signal. The only differences between system 600 of FIG. 5 a andgeneral prior art system 100 of FIG. 1 are; 1) front end block 610 isnow shown as an RF front end, 2) power normalization block 20 has beenreplaced by AGC 620, 3) demodulation block 70 has been replaced with FMdetector 670, and 4) demultiplexer block 671 has been added. FIG. 5 b issimilar to FIG. 5 a, except that AGC 720 has been moved after ADC 730,and is implemented digitally rather than analog. Performance of system600 of FIG. 5 a and system 700 of FIG. 5 b are otherwise identical, andonly operation of system 600 will be discussed further here.

FM detector 670 demodulates the FM signal, and demultiplexer 671 splitsthe demodulated signal into separate left and right channel audiosignals. Demultiplexer 671 can be thought of as providing a datadecoding function. This system works in the same manner as was describedfor general system 100 of FIG. 1. System 600 adjusts the coefficientsignals of transversal filter 640 in an attempt to make the output oftransversal filter 640 have a constant modulus characteristic.

The invention is particularly advantageous when used in an FM receiver.It is often the case that adjacent channel interference will be presentin FM receivers, especially for mobile receivers (such as in vehicles).Furthermore, the relative signal strengths of adjacent channels in amobile receiver can vary significantly, as motion of the receiver causesthe relative transfer functions between the source of desired andinterfering signals with respect to the receiver to vary.

FIG. 6 a is a block diagram of the exemplary embodiment of the currentinvention used as an RF stereo FM receiver. This system is very similarto the system of FIG. 2 a in operation, with the following smallstructural differences; 1) front end 210 has been replaced with RF frontend 810; 2) downconverting ADC 830 has replaced ADC 230; 3) powernormalization block 220 has been replaced by AGC 820; 4) FM detector 870is used in combination with low pass filter 875 replacing SMFD 272; 5)FM detector 870 used as part of the SMFD also replaces data demodulator270, (because the modulation of interest here is frequency modulation)and; 6) demultiplexing function 871 has been added to extract the stereoleft and right channel signals from the output of the demodulator. FIG.6 b is essentially identical to the system of FIG. 6 a, except thatpower normalization in the form of an AGC is located after the ADC, andis implemented in digital form. Operation of systems 800 and 900 isessentially identical, except for the location of the AGC function. As aresult, only operation of system 800 will be described. Operation ofsystem 900 can be directly inferred from the description of system 800.

Downconverting ADC 830 is shown to illustrate that the adaptive filterand mode control functions are operating at baseband frequencies.Downconverting ADC's are well known in the prior art, and will not bedescribed in detail here. A downconverting ADC simultaneouslyaccomplishes digitization of a signal and frequency conversion (from RFor IF to baseband). Separate steps of digitization and down conversioncould also be used, and the invention is not limited to how thesespecific functions are accomplished.

Some blocks have been omitted for clarity. For example, antialiasfilters, D/A converters and reconstruction filters have not been shown.One skilled in the art can implement the necessary processing steps toeffectively convert signals as needed between analog and digital forms.In the system of FIG. 6 b, a logical place to perform D/A conversionwould be in the path between FM detector 870 and de-multiplexer 871.This arrangement would allow the entire adaptive algorithm to beperformed completely in the digital domain, without an additional A/D(which would be needed if the FM detector were implemented as an analogfunction.

It is evident that those skilled in the art may now make numerousmodifications of and departures from the apparatus and techniques hereindisclosed without departing from the inventive concepts. Consequently,the invention is to be construed as embracing each and every novelfeature and novel combination of features present in or possessed by theapparatus and techniques herein disclosed and limited solely by thespirit and scope of the appended claims.

1-6. (canceled)
 7. An adaptive filter comprising: mode circuitry to, ina first mode, whiten a received signal that includes a signal componentof interest and an interfering signal component and, in a second mode,equalize characteristics of a transmission channel on which the receivedsignal is received and a mechanism to control the mode of the modecircuitry between the first mode and the second mode.
 8. The adaptivefilter of claim 7 in which the mode circuitry includes first modecircuitry to perform the whitening and second mode circuitry to performthe equalizing.
 9. The adaptive filter of claim 7 wherein the mechanismcontrols the mode in response to the interfering signal component. 10.The adaptive filter of claim 7 also including a detector to detect amean spectral frequency of the received signal.
 11. The adaptive filterof claim 10 wherein the detector comprises an instantaneous frequencydetector.
 12. The adaptive filter of claim 11 wherein the instantaneousfrequency detector includes an FM detector.
 13. The adaptive filter ofclaim 11 wherein the instantaneous frequency detector includes anaveraging circuit.
 14. The adaptive filter of claim 11 wherein theinstantaneous frequency detector includes a power normalizationcircuitry.
 15. The adaptive filter of claim 7 wherein the mechanism isresponsive to an output signal of the adaptive filter.
 16. The adaptivefilter of claim 7 wherein the signal comprises an FM broadcast signal.17. The adaptive filter of claim 7 wherein the mechanism controlscharacteristics of the first mode and the second mode.
 18. The adaptivefilter of claim 7 wherein the mechanism controls the mode based on arelative strength between the signal component of interest and theinterfering signal component.
 19. A method comprising: using an adaptivefilter to selectively sometimes whiten a received signal that includes asignal component of interest and an interfering signal component and, atother times, equalize characteristics of a transmission channel on whichthe received signal is received.
 20. The method of claim 19 wherein theusing selectively is done in response to the interfering signalcomponent of the received signal.
 21. The method of claim 20 wherein theusing selectively converges the adaptive filter to one of at least onedesired local minima that favors the signal component of interest andbelongs to an error surface, the error surface also having at least oneundesired local minima favoring the interfering signal component. 22.The method of claim 21 wherein the using selectively depends on arelative strength between the signal component of interest and theinterfering signal component.
 23. The method of claim 22 includingdetermining the relative strength based on a mean spectral frequency ofthe received signal.
 24. The method of claim 22 including determiningthe relative strength based on a mean spectral frequency of an output ofthe adaptive filter.
 25. The method of claim 19 in which the whiteningis selected when the interfering signal component is stronger than thesignal component of interest.
 26. The method of claim 20 wherein theusing selectively includes initializing coefficients of the adaptivefilter differently for the whitening and for the equalizing to cause theadaptive filter to converge to a solution favoring the signal componentof interest.
 27. The method of claim 19 including analyzing the receivedsignal and in which the using selectively includes attenuating theinterfering signal component with respect to the signal component ofinterest based on the analysis.
 28. The method of claim 27 wherein theanalyzing includes a strength of the signal component of interestrelative to a strength of the interfering signal component.
 29. Themethod of claim 28 including determining a relative strength based on amean spectral frequency of the received signal.